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Decoding Explainable AI

Navigating Industry 4.0 and Anticipating Industry 5.0

A Practical Approach

Applied Machine Learning in Chemical Process Engineering

Powered by Hyperscale Computing and Next Generation Networks

AI-Driven Smart Healthcare

Frameworks and Applications

Human-Centered AI

Artificial Intelligence and Machine Learning in Data Science and Analytics

Technology-Assisted Physical Gait Rehabilitation: How Robotics, Data Science, and Neuroscience are Changing Modern Physical Therapy delves into the integration of advanced computational modeling, robotic systems, data analytics, and neuroscientific principles to revolutionize physical gait therapy. This book addresses rehabilitation techniques for conditions like stroke, traumatic brain injury, and incomplete spinal cord injury, showcasing a multidisciplinary approach that brings together expertise from various domains. This comprehensive volume gathers insights from leading experts in robotics, human biomechanics, physical therapy, neuroscience, engineering, and medicine. It discusses current advancements and future directions in technology-assisted gait therapy, emphasizing the synergy of interdisciplinary collaboration to achieve breakthroughs in rehabilitation techniques. The book also highlights ongoing work and the potential developments necessary to foster significant progress in this field.

How Robotics, Data Science, and Neuroscience are Changing Modern Physical Therapy

Technology-Assisted Physical Gait Rehabilitation

Wendepunkte in der Analogen und Digitalen Welt

Eeg Signal Classification Using Machine Learning

Intelligent IoT-based Diagnostic and Assistive Systems for Neurological Disorders

A story of unexpected love at the end of the world

Some Body Like Me

An expert compilation of on-device training techniques, regulatory frameworks, and ethical considerations of TinyML design and development In Tiny Machine Learning: Design Principles and Applications, a team of distinguished researchers delivers a comprehensive discussion of the critical concepts, design principles, applications, and relevant issues in Tiny Machine Learning (TinyML). Expert contributors introduce a new low power resource, offering vast applications in IoT devices with system-algorithm co-design. Tiny Machine Learning explores TinyML paradigms and enablers, TinyML for anomaly detection, and the learning panorama under TinyML. Readers will find explanations of TinyML devices and tools, power consumption and memory in IoT microcontrollers, and lightweight frameworks for TinyML. The book also describes TinyML techniques for real-time and environmental applications. Additional topics covered in the book include: <ul> <li>A thorough introduction to security and privacy techniques for TinyML devices, including the implementation of novel security schemes</li> <li>Incisive explorations of power consumption and memory in IoT MCUs, including ultralow-power smart IoT devices with embedded TinyML</li> <li>Practical discussions of TinyML research targeting microcontrollers for data extraction and synthesis</li> </ul> Perfect for industry and academic researchers, scientists, and engineers, Tiny Machine Learning will also benefit lecturers and graduate students interested in machine learning.

Tiny Machine Learning: Design Principles and Applications

AI Overlords, Space Empires, and Silicon Valley's Crusade to Control the Fate of Humanity

More Everything Forever

Artificial Intelligence for Smart Internet of Things Applications

Quantum Machine Learning

Decoding Explainable AI

Weitere Titel zum Thema